Electronic Journal of Plant Breeding (Sep 2023)

Exploration of potential donors for machine-amenable traits in desi chickpea (Cicer arietinum L.): towards enhancing agricultural automation and efficiency

  • M. Himakara Datta1 , Shubham Yadu1 , Kanushree Nandedkar1 , Suman Rawte1 , Ritu R. Saxena*1 and Ravi R. Saxena2

DOI
https://doi.org/10.37992/2023.1403.130
Journal volume & issue
Vol. 14, no. 3
pp. 1246 – 1254

Abstract

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The importance of machine-harvestable traits in chickpeas lies in their capacity to enhance and streamline the harvesting process. By employing machine harvesters, farmers can enjoy numerous advantages over manual harvesting, as it becomes a more efficient and economically viable option. With the aim of achieving this objective, a total of 36 desi chickpea genotypes were selected for a comprehensive study on genetic variability, correlation, and path coefficient analysis. The analysis of variance conducted on 12 distinct characters revealed significant variation, indicating the presence of diversity among these traits. Several traits exhibited significant variability with high PCV and GCV. The height of the first pod, plant height, and hundred seed weight displayed substantial heritability and genetic advance. Remarkably, hundred seed weight was the sole trait with high values of PCV, GCV, heritability, and genetic advance mean (GAM). Taller plants with higher first pod height (HOFP and PH) are associated with increased performance in traits like Plot Yield (PLYG), Harvest Index (HI), Hundred Seed Weight (HSW), Biological Yield (BY), and Number of Secondary Branches (NSB). Genotypic path analysis revealed that both the hundred seed weight and number of secondary branches demonstrated positive direct effects on the height of the first pod. Principal component analysis divided the traits into 12 PCs, where the first four PCs showed eigen values greater than 1 and are responsible for a cumulative variation of 82.9%. Notably, 17 genotypes exhibited a height of the first pod exceeding 30 cm, indicating their suitability for machine harvesting. The genotypes GL 15003, IPCB 2015-132, RVSSG-96, and IPC 2017-253 showed promise for developing machine-harvestable cultivars, based on their height of the first pod and yield attributes.

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